1. Field of the Invention
The present invention relates to an information processing apparatus, and particularly to an information processing apparatus capable of evaluating the rates of contribution of a plurality of pieces of sensor data inputted to a machine learning apparatus and optimizing the configuration of sensors.
2. Description of the Related Art
In the control of machine tools and the like and related fields, machine learning apparatuses are widely used. For example, a machine learning apparatus can determine the presence or absence of a scratch on a workpiece based on input data obtained from a plurality of cameras that capture images of the workpiece. Further, a machine learning apparatus can perform anomaly detection on a motor based on input values such as the output value of a microphone, disturbance value of a current, and the output value of an acceleration sensor or the like.
Machine learning apparatuses are known which recognize a plurality of pieces of input data acquired from sensors and the like using a machine learning-based approach such as classification, regression, or clustering. Techniques for performing machine learning by combining a plurality of pieces of sensor data in this way are called sensor fusion. Some machine learning apparatuses that realize sensor fusion perform machine learning by receiving feature values such as SIFT and SURF extracted from data directly outputted by sensors and the like (such data will hereinafter be referred to as raw data), and some others perform machine learning by directly receiving raw data by deep machine learning.
One example of sensor fusion technique is described in Japanese Patent Application Laid-Open No. 6-102217. In that technique, output signals of a plurality of gas sensors are inputted to a neural network, and the type of gas is identified based on identification patterns learned by the neural network in advance.
With sensor fusion, by inputting various kinds of sensor data in combination, high-accuracy processing such as learning, recognition, or estimation can be realized. However, among a plurality of pieces of sensor data, an approach for systematically identifying sensor data having a high or low rate of contribution to the result of learning, recognition, or estimation and optimizing the configuration of sensors so that required performance may be satisfied is not provided yet. For example, if sensor data having a low rate of contribution to the result of learning, recognition, or estimation can be identified, monetary costs such as the prices of sensors themselves and the cost required for sensor data acquisition and the like can be reduced. Further, time, load, and the like required for measurement, acquisition, and processing of data can be reduced, and the result can be outputted fast.